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Models predicting liver cancer are only available for the high risk population with hepatitis carriers but not for the general population. we developed risk prediction models for liver cancer using data collected from a large health screening program in Taiwan with long term follow up. The cohort included 444,023 subjects at baseline. During a median follow up time of 7.4 years, 1,668 subjects developed liver cancer. Epidemiologic data, medical history and routine blood panel including serum transaminases data were collected with optional additional testing for HBV or HCV status. Liver cancer cases were ascertained by computerized record linkage with both the National Cancer Registry and the National Death Certification profiles in Taiwan. Stepwise Cox regression analysis was performed to identify significant predictors in the multivariate models. Individualized risk of developing liver cancer in 10 years was calculated from baseline probability and relative risk profile estimated from the Cox regression model. Models were developed separately to provide risk prediction for subjects who chose to have HCV tested (130,533 subjects at baseline and 416 liver cancer cases) and who chose otherwise (313,490 subjects and 1,252 liver cancer cases). Since the results for both sub-cohorts are comparable and we only reported results for the sub-cohort with HCV tested. In the sub-cohort with HCV tested, the model with only questionnaire data identified significant main effects for male gender, older age, prior history of diabetes, pack year of smoking, alcohol use, and physical inactivity. This epidemiologic model had an AUC of 0.798 (95% CI=0.772-0.815) for 10-year risk prediction. With only data from measures of Transaminases, the model achieved an AUC of 0.927 (95% CI=0.911-0.938), a significant increase as compared to the model with only epidemiologic variables. The addition of HBV improved the AUC to 0.936 (95% CI=0.913-0.958). Similar results were obtained for sub-cohort without HCV testing. The addition of HCV status in combination with HBV status, the AUC improve to 0.941 (95% CI=0.918-0.967). Among models using key history and blood panels, the use of serum Transaminases only, available in routine health check-ups, was able to provide most information needed in predicting liver cancer in the general public. Additional testing of HBV and /or HCV further increased the prediction power. This simple transaminase-based model for the general public can be valuable in clinical practice for identifying high risk individuals. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5509. doi:1538-7445.AM2012-5509

Background: Attention has been focused on the increased risk of high cholesterol, but the effect of low cholesterol remains unclear and receives little attention. Method: The study cohort, 537,430 adults in Taiwan who went through a medical screening program between 1996 and 2008, was followed up for an average of 8.5 years for mortality and cancer incidence, with 18,745 deaths and 17,714 cancer cases identified. Low and very low cholesterol or LDL were defined as < 180 and Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-324. doi:1538-7445.AM2012-LB-324